A robust video zero-watermarking based on deep convolutional neural network and self-organizing map in polar complex exponential transform domain

Gao, Yumei; Kang, Xiaobing; Chen, Yajun

doi:10.1007/s11042-020-09904-4

Cited by 15 publications

(8 citation statements)

References 31 publications

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“…e Euclidean distance between the weights of different neurons in the competitive layer of the feature vector and SOM neural network is obtained [24][25][26][27][28][29][30][31]. According to the Euclidean distance, the feature vectors are classified into different neurons; the last neuron is traversed to find out the nearest feature vector of each non-control neuron as the key frame [32][33][34][35][36][37][38][39].…”

Section: Human Action Recognition Based On Voting Strategy Of Multi-feature Classification Results Combined With Sommentioning

confidence: 99%

[Retracted] Human Action Recognition in Smart Cultural Tourism Based on Fusion Techniques of Virtual Reality and SOM Neural Network

2021

Computational Intelligence and Neuroscience

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Smart cultural tourism is the development trend of the future tourism industry. Virtual reality is an important tool to realize smart tourism. The reality of virtual reality mainly comes from human-computer interaction, which is closely related to human action recognition technology. Therefore, the research takes human action recognition as the research direction, uses a self-organizing mapping network (SOM) neural network to extract the key frame of action video, combines it with multi-feature vector method to recognize human action, and compares the recognition rate and user satisfaction of different recognition methods. The results show that the recognition rate of multi-feature voting human action recognition algorithm based on SOM neural network is 93.68% on UT-Kinect action, 59.06% on MSRDailyActivity3D, and the overall action recognition time is only 3.59 s. Within six months, the total profit of human-computer interactive virtual reality tourism project with SOM neural network multi-eigenvector as the core algorithm reached 422,000 yuan, and 88% of users expressed satisfaction after use. It shows that the proposed method has a good recognition rate and can give users effective feedback in time. It is hoped that this research has a certain reference value in promoting the development of human motion recognition technology.

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Section: Human Action Recognition Based On Voting Strategy Of Multi-feature Classification Results Combined With Sommentioning

confidence: 99%

[Retracted] Human Action Recognition in Smart Cultural Tourism Based on Fusion Techniques of Virtual Reality and SOM Neural Network

2021

Computational Intelligence and Neuroscience

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“…This has led researchers towards using adaptive methods, such as DNN, for this purpose. Incorporating DNN into the issues of stenography and video watermarking allow a high probability of recovering the watermark and keeping the quality of the watermarked image in high quality [ 38 , 39 , 40 ] by being able to select optimal image composition and decomposition methods as a result of the training process [ 41 , 42 , 43 , 44 , 45 , 46 , 47 ].…”

Section: Literature Reviewmentioning

confidence: 99%

High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

Kaczyński

Piotrowski

Pietrow

2022

Sensors

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This article presents a method for transparent watermarking of high-capacity watermarked video under H.265/HEVC (High-Efficiency Video Coding) compression conditions while maintaining high-quality encoded image. The aim of this paper is to present a method for watermark embedding using neural networks under conditions of subjecting video to lossy compression of the HEVC codec using the YUV420p color model chrominance channel for watermarking. This paper presents a method for training a deep neural network to embed a watermark when a compression channel is present. The discussed method is characterized by high accuracy of the video with an embedded watermark compared to the original. The PSNR (peak signal-to-noise ratio) values obtained are over 44 dB. The watermark capacity is 96 bits for an image with a resolution of 128 × 128. The method enables the complete recovery of a watermark from a single video frame compressed by the HEVC codec within the range of compression values defined by the CRF (constant rate factor) up to 22.

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“…Despite the good number of existing techniques of deep learning-based watermarking proposed for images, video watermarking based on deep learning has only recently begun to be explored and is still an open problem. In fact, as far as we know, there are only a very few number of video watermarking techniques based on deep learning in the literature [16,26,35,40,43,56,59,65,91,97] that appeared since 2019.…”

Section: Deep Learning-based Video Watermarking Reviewmentioning

confidence: 99%

Deep Learning-Based Watermarking Techniques Challenges: A Review of Current and Future Trends

Ben Jabra,

Ben Farah

2024

Circuits Syst Signal Process

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The digital revolution places great emphasis on digital media watermarking due to the increased vulnerability of multimedia content to unauthorized alterations. Recently, in the digital boom in the technology of hiding data, research has been tending to perform watermarking with numerous architectures of deep learning, which has explored a variety of problems since its inception. Several watermarking approaches based on deep learning have been proposed, and they have proven their efficiency compared to traditional methods. This paper summarizes recent developments in conventional and deep learning image and video watermarking techniques. It shows that although there are many conventional techniques focused on video watermarking, there are yet to be any deep learning models focusing on this area; however, for image watermarking, different deep learning-based techniques where efficiency in invisibility and robustness depends on the used network architecture are observed. This study has been concluded by discussing possible research directions in deep learning-based video watermarking.

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A robust video zero-watermarking based on deep convolutional neural network and self-organizing map in polar complex exponential transform domain

Cited by 15 publications

References 31 publications

[Retracted] Human Action Recognition in Smart Cultural Tourism Based on Fusion Techniques of Virtual Reality and SOM Neural Network

[Retracted] Human Action Recognition in Smart Cultural Tourism Based on Fusion Techniques of Virtual Reality and SOM Neural Network

High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

Deep Learning-Based Watermarking Techniques Challenges: A Review of Current and Future Trends

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